def _run_interface(self, runtime):
vol1_nii = nb.load(self.inputs.volume1)
vol2_nii = nb.load(self.inputs.volume2)
if isdefined(self.inputs.mask1):
mask1_nii = nb.load(self.inputs.mask1)
mask1_nii = nb.Nifti1Image(nb.load(self.inputs.mask1).get_data() == 1, mask1_nii.get_affine(),
mask1_nii.get_header())
else:
mask1_nii = None
if isdefined(self.inputs.mask2):
mask2_nii = nb.load(self.inputs.mask2)
mask2_nii = nb.Nifti1Image(nb.load(self.inputs.mask2).get_data() == 1, mask2_nii.get_affine(),
mask2_nii.get_header())
else:
mask2_nii = None
histreg = HistogramRegistration(from_img = vol1_nii,
to_img = vol2_nii,
similarity=self.inputs.metric,
from_mask = mask1_nii,
to_mask = mask2_nii)
self._similarity = histreg.eval(Affine())
return runtime
def _run_interface(self, runtime):
vol1_nii = nb.load(self.inputs.volume1)
vol2_nii = nb.load(self.inputs.volume2)
if isdefined(self.inputs.mask1):
mask1_nii = nb.load(self.inputs.mask1)
mask1_nii = nb.Nifti1Image(
nb.load(self.inputs.mask1).get_data() == 1,
mask1_nii.get_affine(), mask1_nii.get_header())
else:
mask1_nii = None
if isdefined(self.inputs.mask2):
mask2_nii = nb.load(self.inputs.mask2)
mask2_nii = nb.Nifti1Image(
nb.load(self.inputs.mask2).get_data() == 1,
mask2_nii.get_affine(), mask2_nii.get_header())
else:
mask2_nii = None
histreg = HistogramRegistration(from_img=vol1_nii,
to_img=vol2_nii,
similarity=self.inputs.metric,
from_mask=mask1_nii,
to_mask=mask2_nii)
self._similarity = histreg.eval(Affine())
return runtime
def _run_interface(self, runtime):
from nipy.algorithms.registration.histogram_registration import (
HistogramRegistration, )
from nipy.algorithms.registration.affine import Affine
vol1_nii = nb.load(self.inputs.volume1)
vol2_nii = nb.load(self.inputs.volume2)
if isdefined(self.inputs.mask1):
mask1 = nb.load(self.inputs.mask1).get_data() == 1
else:
mask1 = None
if isdefined(self.inputs.mask2):
mask2 = nb.load(self.inputs.mask2).get_data() == 1
else:
mask2 = None
histreg = HistogramRegistration(
from_img=vol1_nii,
to_img=vol2_nii,
similarity=self.inputs.metric,
from_mask=mask1,
to_mask=mask2,
)
self._similarity = histreg.eval(Affine())
return runtime
def _run_interface(self, runtime):
from nipy.algorithms.registration.histogram_registration import HistogramRegistration
from nipy.algorithms.registration.affine import Affine
vol1_nii = nb.load(self.inputs.volume1)
vol2_nii = nb.load(self.inputs.volume2)
if isdefined(self.inputs.mask1):
mask1 = nb.load(self.inputs.mask1).get_data() == 1
else:
mask1 = None
if isdefined(self.inputs.mask2):
mask2 = nb.load(self.inputs.mask2).get_data() == 1
else:
mask2 = None
histreg = HistogramRegistration(
from_img=vol1_nii,
to_img=vol2_nii,
similarity=self.inputs.metric,
from_mask=mask1,
to_mask=mask2)
self._similarity = histreg.eval(Affine())
return runtime
def _run_interface(self, runtime):
from nipy.algorithms.registration.histogram_registration import (
HistogramRegistration, )
from nipy.algorithms.registration.affine import Affine
vol1_nii = nb.load(self.inputs.volume1)
vol2_nii = nb.load(self.inputs.volume2)
dims = vol1_nii.get_data().ndim
if dims == 3 or dims == 2:
vols1 = [vol1_nii]
vols2 = [vol2_nii]
if dims == 4:
vols1 = nb.four_to_three(vol1_nii)
vols2 = nb.four_to_three(vol2_nii)
if dims < 2 or dims > 4:
raise RuntimeError(
"Image dimensions not supported (detected %dD file)" % dims)
if isdefined(self.inputs.mask1):
mask1 = nb.load(self.inputs.mask1).get_data() == 1
else:
mask1 = None
if isdefined(self.inputs.mask2):
mask2 = nb.load(self.inputs.mask2).get_data() == 1
else:
mask2 = None
self._similarity = []
for ts1, ts2 in zip(vols1, vols2):
histreg = HistogramRegistration(
from_img=ts1,
to_img=ts2,
similarity=self.inputs.metric,
from_mask=mask1,
to_mask=mask2,
)
self._similarity.append(histreg.eval(Affine()))
return runtime
def _run_interface(self, runtime):
if not self._have_nipy:
raise RuntimeError('nipy is not installed')
from nipy.algorithms.registration.histogram_registration import HistogramRegistration
from nipy.algorithms.registration.affine import Affine
vol1_nii = nb.load(self.inputs.volume1)
vol2_nii = nb.load(self.inputs.volume2)
dims = vol1_nii.get_data().ndim
if dims == 3 or dims == 2:
vols1 = [vol1_nii]
vols2 = [vol2_nii]
if dims == 4:
vols1 = nb.four_to_three(vol1_nii)
vols2 = nb.four_to_three(vol2_nii)
if dims < 2 or dims > 4:
raise RuntimeError('Image dimensions not supported (detected %dD file)' % dims)
if isdefined(self.inputs.mask1):
mask1 = nb.load(self.inputs.mask1).get_data() == 1
else:
mask1 = None
if isdefined(self.inputs.mask2):
mask2 = nb.load(self.inputs.mask2).get_data() == 1
else:
mask2 = None
self._similarity = []
for ts1, ts2 in zip(vols1, vols2):
histreg = HistogramRegistration(from_img=ts1,
to_img=ts2,
similarity=self.inputs.metric,
from_mask=mask1,
to_mask=mask2)
self._similarity.append(histreg.eval(Affine()))
return runtime
